Leetcode MEDIUM 3322 - Premier League Table Ranking III - RANKING in SQL | Everyday Data Science
Premier League Table Ranking III - Video Solution
Watch the video solution for Premier League Table Ranking III, a medium level problem involving Database. This walkthrough by Everyday Data Science has 508 views views. Want to try solving it yourself? Practice on FleetCode or read the detailed text solution.
Problem Statement
Table: SeasonStats
+------------------+---------+ | Column Name | Type | +------------------+---------+ | season_id | int | | team_id | int | | team_name | varchar | | matches_played | int | | wins | int | | draws | int | | losses | int | | goals_for | int | | goals_against | int | +------------------+---------+ (season_id, team_id) is the unique key for this table. This table contains season id, team id, team name, matches played, wins, draws, losses, goals scored (goals_for), and goals conceded (goals_against) for each team in each season.
Write a solution to calculate the points, goal difference, and position for each team in each season. The position ranking should be determined as follows:
- Teams are first ranked by their total points (highest to lowest)
- If points are tied, teams are then ranked by their goal difference (highest to lowest)
- If goal difference is also tied, teams are then ranked alphabetically by team name
Points are calculated as follows:
3points for a win1point for a draw0points for a loss
Goal difference is calculated as: goals_for - goals_against
Return the result table ordered by season_id in ascending order, then by position in ascending order, and finally by team_name in ascending order.
The query result format is in the following example.
Example:
Input:
SeasonStats table:
+------------+---------+-------------------+----------------+------+-------+--------+-----------+---------------+ | season_id | team_id | team_name | matches_played | wins | draws | losses | goals_for | goals_against | +------------+---------+-------------------+----------------+------+-------+--------+-----------+---------------+ | 2021 | 1 | Manchester City | 38 | 29 | 6 | 3 | 99 | 26 | | 2021 | 2 | Liverpool | 38 | 28 | 8 | 2 | 94 | 26 | | 2021 | 3 | Chelsea | 38 | 21 | 11 | 6 | 76 | 33 | | 2021 | 4 | Tottenham | 38 | 22 | 5 | 11 | 69 | 40 | | 2021 | 5 | Arsenal | 38 | 22 | 3 | 13 | 61 | 48 | | 2022 | 1 | Manchester City | 38 | 28 | 5 | 5 | 94 | 33 | | 2022 | 2 | Arsenal | 38 | 26 | 6 | 6 | 88 | 43 | | 2022 | 3 | Manchester United | 38 | 23 | 6 | 9 | 58 | 43 | | 2022 | 4 | Newcastle | 38 | 19 | 14 | 5 | 68 | 33 | | 2022 | 5 | Liverpool | 38 | 19 | 10 | 9 | 75 | 47 | +------------+---------+-------------------+----------------+------+-------+--------+-----------+---------------+
Output:
+------------+---------+-------------------+--------+-----------------+----------+ | season_id | team_id | team_name | points | goal_difference | position | +------------+---------+-------------------+--------+-----------------+----------+ | 2021 | 1 | Manchester City | 93 | 73 | 1 | | 2021 | 2 | Liverpool | 92 | 68 | 2 | | 2021 | 3 | Chelsea | 74 | 43 | 3 | | 2021 | 4 | Tottenham | 71 | 29 | 4 | | 2021 | 5 | Arsenal | 69 | 13 | 5 | | 2022 | 1 | Manchester City | 89 | 61 | 1 | | 2022 | 2 | Arsenal | 84 | 45 | 2 | | 2022 | 3 | Manchester United | 75 | 15 | 3 | | 2022 | 4 | Newcastle | 71 | 35 | 4 | | 2022 | 5 | Liverpool | 67 | 28 | 5 | +------------+---------+-------------------+--------+-----------------+----------+
Explanation:
- For the 2021 season:
- Manchester City has 93 points (29 * 3 + 6 * 1) and a goal difference of 73 (99 - 26).
- Liverpool has 92 points (28 * 3 + 8 * 1) and a goal difference of 68 (94 - 26).
- Chelsea has 74 points (21 * 3 + 11 * 1) and a goal difference of 43 (76 - 33).
- Tottenham has 71 points (22 * 3 + 5 * 1) and a goal difference of 29 (69 - 40).
- Arsenal has 69 points (22 * 3 + 3 * 1) and a goal difference of 13 (61 - 48).
- For the 2022 season:
- Manchester City has 89 points (28 * 3 + 5 * 1) and a goal difference of 61 (94 - 33).
- Arsenal has 84 points (26 * 3 + 6 * 1) and a goal difference of 45 (88 - 43).
- Manchester United has 75 points (23 * 3 + 6 * 1) and a goal difference of 15 (58 - 43).
- Newcastle has 71 points (19 * 3 + 14 * 1) and a goal difference of 35 (68 - 33).
- Liverpool has 67 points (19 * 3 + 10 * 1) and a goal difference of 28 (75 - 47).
- The teams are ranked first by points, then by goal difference, and finally by team name.
- The output is ordered by season_id ascending, then by rank ascending, and finally by team_name ascending.
Approach Overview
Problem Overview: You are given Premier League team statistics and need to produce the league table ranking. Teams must be ordered by their performance metrics (such as points and tie‑breakers) and assigned a rank following league table rules.
Approach 1: Window Function Ranking (O(n log n) time, O(n) space)
The most practical solution uses SQL window functions. First sort teams by the ranking criteria used in league tables, typically points DESC, then tie‑breakers such as goal_difference DESC and goals_scored DESC. After ordering the rows, apply a ranking function like RANK() or DENSE_RANK() using OVER (ORDER BY ...). The database engine performs the ordering and assigns ranks in a single pass over the sorted result set. Time complexity is O(n log n) due to sorting, and space complexity is O(n) for the intermediate ordered dataset.
This approach is ideal for relational datasets because ranking logic stays inside the query. Window functions are optimized by most SQL engines and avoid complicated self‑joins or subqueries. If you work with analytics queries or leaderboard problems, this pattern appears frequently. See related patterns in database queries and SQL window functions.
Approach 2: Pandas Window Ranking (O(n log n) time, O(n) space)
In a Pandas environment, replicate the same logic using DataFrame sorting followed by ranking. First call sort_values() on the ranking columns in descending order. Then use rank() with the appropriate method (such as dense or min) to generate league positions. Pandas internally sorts the rows and computes ranks across the ordered dataset. Time complexity remains O(n log n) because sorting dominates, and space complexity is O(n) for the DataFrame.
This version is useful when the dataset is already loaded into Python for analysis. It mirrors SQL logic closely, which makes it easy to translate between query solutions and data analysis workflows. More patterns like this appear in pandas data manipulation problems.
Recommended for interviews: The window function solution is the expected answer. Interviewers want to see that you know how to compute rankings using RANK() or DENSE_RANK() instead of manual comparisons or nested queries. Understanding how ordering and ranking interact shows strong SQL fundamentals.
Complexity Analysis
| Approach | Time | Space | When to Use |
|---|---|---|---|
| SQL Window Function Ranking | O(n log n) | O(n) | Best choice for SQL interview questions and leaderboard-style ranking queries |
| Pandas Sort + Rank | O(n log n) | O(n) | When working with the dataset in Python for analytics or preprocessing |